Predicting urinary bladder voiding by means of a linear discriminant analysis: Validation in rats. (January 2020)

• Record Type: Journal Article
• Authors: Tantin, A.; Bou Assi, E.; van Asselt, E.; Hached, S.; Sawan, M.
• Is Part Of: Biomedical signal processing and control. Volume 55(2020)
• Publication Date: 2020-01
• Subjects: Bladder dysfunctions -- Bladder pressure -- Urinary voiding prediction -- Machine learning -- LDA classifier; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101667
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Predicting urinary bladder voiding is still not perfectly mastered, but it could be of great use for patients suffering for bladder dysfunctions. We developed a machine learning algorithm to forecast voiding based on a linear discriminant analysis. With this study, accurate urinary bladder voiding forecasting could be implemented in closed-loop advisory/intervention devices. Abstract: Aims: The objective of this work is to investigate whether changes in bladder pressure's patterns can be used to forecast voiding events in rats with both normal and overactive detrusor. Methods: A voiding forecasting algorithm based on machine learning was developed. Raw pressure curves as well as their corresponding power bands were used as inputs to a linear discriminant analysis classifier. Performance was evaluated on held-out test data and was statistically validated via comparison to random predictors. Results: Using the band-power feature, 93% and 99% of the alarms were respectively generated within 95 s before voiding for normal and hyperactive bladder conditions respectively. The same algorithm was assessed using the band-power feature. It showed performances achieving respective success rates of 99% and 97% for normal and hyperactive bladder condition respectively with alarms generated within 45 s before voiding. Conclusions: We have demonstrated the feasibility of detecting the pre-voiding periods in rats with normal and overactive bladders with a high success rate. Significance: To our knowledge, this is the first study that demonstrates the possibility of predicting voiding in rats with a machine learning algorithm based on a Linear Discriminant Analysis. Our work was compared to other relevant studies and showed better results. With this study, accurate urinary bladder voiding forecasting could be implemented in closed-loop advisory/intervention devices.